Performance comparison of neural network training methods based on wavelet packet transform for classification of five mental tasks
Vijay Khare, Jayashree Santhosh, Sneh Anand, Manvir Bhatia
DOI: 10.4236/jbise.2010.36083   PDF    HTML     4,566 Downloads   8,214 Views   Citations


In this study, performances comparison to discriminate five mental states of five artificial neural network (ANN) training methods were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw electroencephalogram (EEG) signals. The five ANN training methods used were (a) Gradient Descent Back Propagation (b) Levenberg-Marquardt (c) Resilient Back Propagation (d) Conjugate Learning Gradient Back Propagation and (e) Gradient Descent Back Propagation with movementum.

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Khare, V. , Santhosh, J. , Anand, S. and Bhatia, M. (2010) Performance comparison of neural network training methods based on wavelet packet transform for classification of five mental tasks. Journal of Biomedical Science and Engineering, 3, 612-617. doi: 10.4236/jbise.2010.36083.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Wolpaw, J.R., Birbaumer, N., Mc Farland, D.J., Plurtscheller, G. and Vaughan, T.M. (2002) Brain computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791.
[2] Pfurtschelle, G., Flotzinger, D. and Kalcher, J. (1993) Brain computer interface-A new communication device for handicapped people. Journal of Microcomputer Applications, 16, 293-299.
[3] Wolpaw, J.R., Vaughan, T.M. and Donchin, E. (1996) EEG based communication prospects and problems. IEEE Transactions on Rehabilitation Engineering, 4, 425-430.
[4] Lotte, F., Congedo, M., Lecuyer, A., Lamarche, F. and Arnaldi, B. (2007) A review of classification algorithms for EEG bases brain computer interface. Journal of neural Engineering, 4, 1-13.
[5] Wolpaw, J.R., Leob, G.E., Allison, B.Z., Donchin, E. and Turner, J.N. (2006) BCI Meeting 2005-Wokshop on signals and rerecording methods. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14, 138- 141.
[6] Anderson, C.W., Stolz, E.A. and Shamsunder, S. (1998) Multivariable autoregressive model for classification of spontaneous electroencephalogram during mental tasks. IEEE Transactions on Biomedical Engineering, 45, 277- 278.
[7] Boostani, R., Graimann, B., Moradi, M.H. and Plurf- scheller, G. (2007) Comparison approach toward finding the best feature and classifier in cue BCI. Journal of Medical and Biological Engineering and Computing, 45, 403-413.
[8] Palaniappan, R. (2006) Utilizing gamma band to improve mental task based brain-Computer interface design. IEEE Transactions on Neural and Rehabilitation Systems Engineering, 14, 299-303.
[9] Keirn, Z.A. and Aunon, J.I. (1990) A new mode of communication between man and his Surroundings. IEEE Transactions on Biomedical Engineering, 37, 1209-1214.
[10] Palaniappan, R. (2005) Brain computer interface design using band powers extracted during mental task. Procee- ding of the 2nd International IEEE EMBS Conference on Neural Engineering, Arlington, 321-324.
[11] Pfurtscheller, G., Neuper, C., Schlogl, A. and Lugger, K. (1998) Separability of EEG signals recorded during right and left motor imagery using adaptive auto regressive parameters. IEEE Transactions on rehabilitation Engin- eering, 6, 316-325.
[12] Santhosh, J., Bhatia, M., Sahu, S. and Anand, S. (2004) Quantitative EEG analysis for assessment to plan a task in ALS patients, a study of executive function (planning) in ALS. Journal of Cognitive Brain Research, 22, 59-66.
[13] Nikolaev, A.R. and Anokhin, A.P. (1998) EEG frequency ranges during reception and mental rotation of two and three dimensional objects. Neuroscience and Bheaviour physiology, 29, 203-223.
[14] Osaka, M. (1984) Peak alpha frequency of EEG during a mental task: Task difficulty and hemisphere difference. Journal of Psychophysiology, 21, 101-105.
[15] Ting, W., Zhenga, Y.G., Bang-huaa, Y. and Hong, S. (2008) EEG feature extraction based on wavelet packet decom-position for brain computer interface. Measurement, Elsevier Journal, 41, 618-625.
[16] Akay, M. (1995) Wavelet in biomedical engineering. Journal of Annals of Biomedical Engineering, 23, 529- 530.
[17] Ravi, K.V.R. and Palaniappan, R. (2006) Neural Network Classification of Late gamma band electroenceph-alogram features, Soft Computing, 10, 163-169.
[18] Cheng, M., Gao, X., Gao, S. and Xu, D. (2002) Design and implementation of a brain computer interface with high transfer rates. IEEE Transactions on Biomedical Engineering, 49, 1181-1186.

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